Quant Research Engineer

Millennium; Hong Kong; On-site; HKD 900,000 - 1,300,000; Full time

Job summary

Millennium in Hong Kong seeks a technically seasoned engineer to own the firm's core infrastructure for quantitative research and trading. You will design, build, and maintain data pipelines and compute environments, ensuring reliability, scalability, and integration of AI-native capabilities into the research stack.

You will collaborate with Quant Researchers and other teams to deploy robust tooling, observability, and automation.

Qualifications

• 3-5 years of professional experience in quantitative development or strong AI/LLM engineering with ownership of complex infrastructure.
• Proven end-to-end ownership of significant trading/research/AI infrastructure.
• Deep expertise in modern C++ and Python in HPC contexts.
• Experience with large-scale data infrastructure (real-time streaming and historical data).
• Strong background in cloud computing (AWS, GCP, or Azure) and parallel computing.

Responsibilities

• Design, build, and maintain core quant pipelines, data infra, and compute environments.
• Ensure reliability, scalability, and performance of critical systems for research and trading.
• Drive architecture for data/compute platform including AI-native capabilities integrated into the stack.
• Collaborate with researchers and dev teams to meet requirements and integrate components.
• Own deployment, monitoring, and health of production and research systems; improve observability and AI-assisted operations.

Skills

C++

Python

Cloud computing

Kubernetes

DevOps

LLM engineering

Agent frameworks

SQL/NoSQL

Tools

Docker

Kubernetes

Apache Spark

Redis

Dask

Terraform

CloudFormation

GitHub Actions

GitLab CI

Job description

Preferred Candidate Profile

• Top-tier academic background from a globally top-20 university (e.g., MIT, Harvard, Princeton, Stanford, Caltech)
• PhD-level training in Computer Science, Engineering, Physics, Mathematics, or Statistics preferred
• Gold medal in a national or international olympiad (IMO, CMO, IOI, NOI, IPhO, CPhO) strongly preferred
• Prior experience at a top-tier quantitative trading firm or a leading AI/technology company preferred
• Demonstrated passion for applying AI — candidates who have built LLM-powered tools into their own research or engineering workflow stand out

Key Responsibilities

Core Infrastructure Ownership

• Design, build, and maintain the firm’s core quant pipelines, data infrastructure, and research and production compute environments.
• Ensure the reliability, scalability, and performance of critical systems central to our research and trading activities.
• Drive the architectural vision for our next-generation data and compute platform - including how AI-native capabilities (LLM services, agentic workflows, retrieval infrastructure) are embedded into the research stack.

Collaboration & Integration

• Partner directly with Quantitative Researchers and other development teams to understand their requirements and integrate new components into the core infrastructure.
• Act as a central point of expertise, facilitating the seamless flow of data and computation across teams and systems.
• Identify where AI can accelerate the research process - from literature ingestion and data exploration to signal prototyping - and build the tooling that makes it routine.
• Establish and enforce rigorous standards for system design, code quality, testing, and deployment.

DevOps & AI-Augmented Operations

• Own the deployment, monitoring, and operational health of production and research systems.
• Implement robust observability, logging, and alerting frameworks; apply AI-assisted techniques (automated log analysis, anomaly detection, intelligent incident triage) to raise the bar on reliability.
• Drive infrastructure-as-code practices and automate operational workflows, leveraging AI coding agents and LLM tooling where they demonstrably improve velocity and quality.

Qualifications & Experience

• 3-5 years of professional experience in a quantitative development role, focused on building and maintaining quantitative research and production pipelines. Alternatively, significant engineering experience in a fast-paced startup — or strong hands-on AI/LLM engineering experience (building production LLM applications, agentic systems, or AI-powered developer tooling) — with demonstrated ownership of complex infrastructure will be considered in lieu of direct quant experience.
• Proven, end-to-end ownership of a significant piece of trading, research, high-performance, or AI infrastructure.
• Deep expertise in modern C++ and Python in a high-performance computing context.
• Demonstrable experience with large-scale data infrastructure (e.g., real-time/streaming and historical tick data).
• Strong background in cloud computing (AWS, GCP, or Azure) and parallel computing paradigms.

Hard Skills & Technical Knowledge

• Broad knowledge of the technology landscape and the judgment to select the right tool for the problem (e.g., KDB+, Apache Spark, Dask, Redis).
• Practical experience applying LLMs and agentic workflows to real engineering or research problems - LLM APIs, agent frameworks, retrieval-augmented generation, and structured output pipelines - with sound judgment about where AI adds value and where determinism must be preserved.
• Proficiency with different database designs - SQL, NoSQL, and distributed file systems.
• Experience with containerization and orchestration technologies (Docker, Kubernetes).
• Strong experience with DevOps practices: infrastructure-as-code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, GitLab CI), and system observability - including familiarity with AI-assisted operations tooling.

Soft Skills

• Exceptional Logical & Reflective Thinking: Ability to deconstruct complex problems and design elegant, effective solutions.
• Proactive Collaboration: A team player who thrives in a collaborative environment and builds strong partnerships.
• High Integrity: Takes initiative and ownership of projects, upholding rigorous ethical standards in handling sensitive data and models.
• Growth Mindset: Innate curiosity and commitment to continuous improvement - including genuine enthusiasm for the rapidly evolving AI landscape and a track record of adopting new tools ahead of the curve.
• Superb Communication: Can articulate complex technical concepts to both technical and non-technical stakeholders.